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arXiv · 2303.00177

Finite-sample Guarantees for Nash Q-learning with Linear Function Approximation

Pedro Cisneros-Velarde·Sanmi Koyejo
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Abstract

Nash Q-learning may be considered one of the first and most known algorithms in multi-agent reinforcement learning (MARL) for learning policies that constitute a Nash equilibrium of an underlying general-sum Markov game. Its original proof provided asymptotic guarantees and was for the tabular case. Recently, finite-sample guarantees have been provided using more modern RL techniques for the tabular case. Our work analyzes Nash Q-learning using linear function approximation -- a representation regime introduced when the state space is large or continuous -- and provides finite-sample guarantees that indicate its sample efficiency. We find that the obtained performance nearly matches an existing efficient result for single-agent RL under the same representation and has a polynomial gap when compared to the best-known result for the tabular case.

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Pedro Cisneros-Velarde, Sanmi Koyejo. 2023-03-01. Finite-sample Guarantees for Nash Q-learning with Linear Function Approximation. https://arxiv.org/abs/2303.00177

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